A Kolmogorov-Arnold Network for Explainable Detection of Cyberattacks on EV Chargers

• URL: http://arxiv.org/abs/2503.02281v1
• Date: Tue, 04 Mar 2025 05:06:39 GMT
• Title: A Kolmogorov-Arnold Network for Explainable Detection of Cyberattacks on EV Chargers
• Authors: Ahmad Mohammad Saber, Max Mauro Dias Santos, Mohammad Al Janaideh, Amr Youssef, Deepa Kundur,
• Abstract summary: This paper presents a novel Kolmogorov-Arnold Network (KAN)-based framework for detecting cyberattacks on EV chargers.<n>The framework effectively differentiates between normal and malicious charging scenarios.<n>Our results demonstrate that the proposed KAN-based approach can accurately detect cyberattacks on EV chargers with Precision and F1-score of 99% and 92%, respectively.
• Score: 4.8875197799836005
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: The increasing adoption of Electric Vehicles (EVs) and the expansion of charging infrastructure and their reliance on communication expose Electric Vehicle Supply Equipment (EVSE) to cyberattacks. This paper presents a novel Kolmogorov-Arnold Network (KAN)-based framework for detecting cyberattacks on EV chargers using only power consumption measurements. Leveraging the KAN's capability to model nonlinear, high-dimensional functions and its inherently interpretable architecture, the framework effectively differentiates between normal and malicious charging scenarios. The model is trained offline on a comprehensive dataset containing over 100,000 cyberattack cases generated through an experimental setup. Once trained, the KAN model can be deployed within individual chargers for real-time detection of abnormal charging behaviors indicative of cyberattacks. Our results demonstrate that the proposed KAN-based approach can accurately detect cyberattacks on EV chargers with Precision and F1-score of 99% and 92%, respectively, outperforming existing detection methods. Additionally, the proposed KANs's enable the extraction of mathematical formulas representing KAN's detection decisions, addressing interpretability, a key challenge in deep learning-based cybersecurity frameworks. This work marks a significant step toward building secure and explainable EV charging infrastructure.

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